A compressive beamforming method

Compressive Sensing (CS) is an emerging area which uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. This paper considers the direction-of-arrival (DOA) estimation problem with an array of sensors using CS. We show that by using random projections of the sensor data, along with a full waveform recording on one reference sensor, a sparse angle space scenario can be reconstructed, giving the number of sources and their DOA’s. The number of projections can be very small, proportional to the number sources. We provide simulations to demonstrate the performance and the advantages of our compressive beamformer algorithm.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Ali Cafer Gurbuz, James H. McClellan, Volkan Cevher
• Keywords: Compressive Sensing, ICASSP 2008, Randomized Projections, Signal Processing, Sparse Angle Space